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Titlebook: Latent Variable Analysis and Signal Separation; 12th International C Emmanuel Vincent,Arie Yeredor,Petr Tichavsky Conference proceedings 20

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書目名稱Latent Variable Analysis and Signal Separation
副標題12th International C
編輯Emmanuel Vincent,Arie Yeredor,Petr Tichavsky
視頻videohttp://file.papertrans.cn/582/581806/581806.mp4
概述Up-to-date results.Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Latent Variable Analysis and Signal Separation; 12th International C Emmanuel Vincent,Arie Yeredor,Petr Tichavsky Conference proceedings 20
描述This book constitutes the proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICS 2015, held in Liberec, Czech Republic, in August 2015. The 61 revised full papers presented – 29 accepted as oral presentations and 32 accepted as poster presentations – were carefully reviewed and selected from numerous submissions. Five special topics are addressed: tensor-based methods for blind signal separation; deep neural networks for supervised speech separation/enhancement; joined analysis of multiple datasets, data fusion, and related topics; advances in nonlinear blind source separation; sparse and low rank modeling for acoustic signal processing.
出版日期Conference proceedings 2015
關鍵詞audio signal processing; augmented statistics; classification; computational imaging; decision trees; dee
版次1
doihttps://doi.org/10.1007/978-3-319-22482-4
isbn_softcover978-3-319-22481-7
isbn_ebook978-3-319-22482-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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Tensors and Latent Variable Modelsnear algebra. A few ideas have been developed independently in the two communities. However, there are still many useful but unexplored links and ideas that could be borrowed from one of the communities and used in the other. We will start our discussion from simple concepts such as independent vari
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Improving Deep Neural Network Based Speech Enhancement in Low SNR Environments-noise-ratio (SNR) environments. Deep Neural Networks (DNN) have recently been successfully adopted as a regression model in SE. Nonetheless, the performance in harsh environments is not always satisfactory because the noise energy is often dominating in certain speech segments causing speech distor
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